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Technical Report 708

The Impact of Soldier Quality on

Performance in the Army

David K. Home

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i• Manpower and Personnel Policy Research Group

SManpower and Personnel Research Laboratory

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LI.J

•, U.S. Army

•:1 Research Institute for the Behavioral and Social Sciences

April 1986

Approved Io¢ public release; distribution unllmiteO.

,•6 11 7 014
U. S. ARMY RESEARCH INSTITUTE

FOR THE BEHAVIORAL AND SOCIAL SCIENCES

A Field Operating Agency under the Jurisdiction of the

Deputy Chief of Staff for Personnel

WM. DARRYL HENDERSON

EDGAR M. JOHNSON COL, IN

Technical Director Comnmnding

Technical review by

Jane Arabian :

Roy Nord ,." "

J "-- :"" ' c:,t 1 o,,' _ . •. . ..

;'- £. ir ,t ion/

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NOTICES

DISTRIBUTION: Primary distribution of this report has been made by ARI. Please address correspondence concerning distribution of reports to: U.S. Army Research Institute for the Behavioral

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NOTE: The findings in this report are not to be construed as an official Department of the Army

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REPORT DOCUMENTATION PAGE BFREA COMPLECTINFORM

1.REPORT NMuSER .GVT ACCESSION NO.1 2. RE IPIENY'S CATALOG NUMBER

ARI Technical Report 708 J 4. TITLE (and Subtitle 10TP)O EOT EID OEE

T!HE IMPACT OF SOLDIER QUALITY ON PERFORMANCE November 1985 - March 1986

IN THE ARMY 6. PERFORMING ORG. REPORT NUMBER

________________________________________MPPRG WP85-14

7. AUTNOR~e) 8. CONTRACT OR GRANT NUM BER(&)

David K. Horne

9. PERFORMING OROANI1XATION NAME AND ADDRESS 10. PROGRAM ELEMENT. PROJECT. TASK

u.S. Army Research Institute for the Behavioral AREA & WORK UNIT NUMBERS

and ocia Scinces2Q263731A792

11. COUTRPLIMNTAR NOTFIESNM N DRS2EOTDT

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i SECURITY CLASIlFICATION OF THIS PAGE (Whna Daea Entered)
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20.. (Continued)

empirical link between AFQT scores and soldier performance. This study presents evidence on that relationship using data from several sources. The first

data set contains written and hands-on tests on several weapons systems from

the Army's training schools. The second data set utilizes the Skill Qualification Tests administered by the Army. The results demonstrate that a statistically significant and positive relationship exists between AFQT scores and

performance measures. These findings are consistent across a wide range of

Military Occupational Specialties.

UNCLASSTFITr)

SECURITY CLASSIfICATIOW OF T"IS PAGE(W144- 0-10 EMrd

iii
Technical Report 708

The Impact of Soldier Quality on

Performance in the Army

David K. Home

Manpower and Personnel Policy Research Group

Curtis L. Gilroy, Chief

Manpower and Personnel Research Laboratory

Newell K. Eaton, Director

U.S. ARMY RESEARCH INSTITUTE FOR THE BEHAVIORAL AND SOCIAL SCIENCES

5001 Eisenhower Avenue, Alexandria, Virginia 22333-5600

Office, Deputy Chief of Staff for Personnel

Department of the Army

April 1986

Army Project Number Manpowei and Personnel

2Q263731A792

ApDroved for Dublic release: distribution unlimited.

zii
ARI Research Reports and Technical Reports are intended for sponsors of

R&D tasks and for other research and military agencies. Any findings ready

for implementation at the time of publication are presented in the last part

of the Brief. Upon completion of a major phase of the task, formal recommendations for official action normally are conveyed to appropriate military

agencies by briefing or Disposition Form.

iv
FOREWORD

The Manpower and Personnel Policy Research Group of the Army Research

Institute (ARI) performs research in the issues of manpower, personnel, and

training of particular significance to the U.S. Army. This research addresses

the issue of the effectiveness of Armed Forces Qualification Test in predicting

manpower performance and was prepared as part of ARI's continuing support for

the Office of the Deputy Chief of Staff for Personnel.

EDGAR M. JOHNSON

Technical Director

I
ACJKNOWLEDGMENTi

The author is an economist in the Manpower and Personnel Policy Research

Group. This paper has benefited from the assistance of MAJ Thomas Daula; the

data were provided by Frances Grafton. Comments and suggestions were also provided by Curtis Gilroy, Lawrence Hanser, Frances Grafton, Edward Schmitz, and

Donald Cox. The views expressed here represent those of the author and not

necessarily those of the aforementioned individuals, the U.S. Army, or the Department of Defense.

vi

-- -- -- -
THE IMPACT OF SOLDIER QUALITY ON PERFORMANCE IN THE ARMY

EXECUTIVE SUMMARY

Requirement:

The Army has been successful in improving its manpower quality over the

last several years. Recruits are scoring higher on the Armed Forces Qualification Test (AFQT) and are more likely to have high school diplomas than in any

year since the inception of the All-Volunteer Force. Recruiting such personnel is expensive, however, as the Army faces increased competition from the

civilian labor market, educational institutions, and the other services for a

shrinking youth population. In order to justify its manpower requirements, the

Army must be able to demonstrate an empirical link between AFQT scores and soldier performance: AFQT is designed to measure trainability. However, its

value as a predictor of performance must be empirically verified. This research presents evidence on that relationship using data from several sources:

the Army's training schools and Skill Qualification Tests (SQT).

Procedure:

The performance measures are modeled in a multivariate regression model,

using an instrumental variables technique to correct for measurement error in

the AFQT variable. Other explanatory variables are sex, race, education, Army

experience, and training.

Findings:

The results of the analysis on the TRASANA training data and the 1983

skill level two SQT data demonstrate that AFQT, a measure of trainability, is

a significant predictor of performance in the Army. The performance and skill

measures used in this study are imperfect, but the consistency of the relationship across types of performance measures and across MOS is impressive. The

analysis of the SQT data is reported for several large representative MOS, but

the pattern is also consistent for almost all MOS having sufficient observations to permit analysis. Holding the effect of other variables constant, AFQT

exerts a positive and significant influence on Army performance. No other

variables are consistently significant across all MOS in both data sets. These

findings are consistent with another study that also documented the positive

impact of AFQT scores on Army soldier performance for tankers.

Utilization of Findings;

The empirical analysis demonstrates that AFQT scores are indeed a significant and consistent determinant of Army performance for a variety of performance measures. The equations indicate how much additional performance, on

average, is associated with an increase in AFQT scores. This information

vii

S. . .. 6 -
supports the current Army policy of recruiting high-quality manpower, because

the higher recruiting costs are offset by increased labor productivity. Earlier versions of this analysis done in collaboration with MAJ Thomas Daula

were used by the Army to support current quality-recruiting goals before

Congress in Defense Manpower Quality, Report to the House and Senate Committees on Armed Services, Volume 11, May 1985, appendixes E and I.

viii

I,*~*~
THE IMPACT OF SOLDIER QUALITY ON PERFORMANCE IN THE ARMY

CONTENTS

Page

ABILITY AND PERFORMANCE . . . . . . . . . . . . . . . . . . . . . . . . . 2

Ability Measurement . . . . . . . . . ................... 2

Performance Measures . . . . . . . . . . . . . . . . . . . . . # . , . 4

THE MODEL . . . . . . . . . . . . ............. . . . . . . 6

DATA .. . . . . . . .. . .. .. . . . . . . . . . . . .......... 9

Training Data . . . . . . . . . . . .................. 9

SQT Data. . . ......... . . . . . ....... . . . . ....... 11

RESULTS . R.e . . ... . . . . . . .. . . . . . . . . . . . . . . . . . . 12

TQTn Results ...................... . ............ . . . . . . 16

CONCLUSIONS . . ...................... . ...... 16

REFERENCES . ..................... . . . . . . . . . . 19

APPENDIX . . . . . . . . . . . . ....................... 20

LIST OF TABLES

Table 1. Training data: Written tests . . . . . . . . . ......... 13

2. Training data: Hands-on tests ................. 14

3. Training data: Lance hands-on test3 ........ ............. .. 15

4. SW instrumental variable regression equations .... ........ .. 17

ix
The Impact of Soldier Quality on Performance In the Army

David K. Horne*

The productivity of an individual on the joh depends ipon such unohservable

personal attributes as ability, motivation, physical coordination, and other joh-specific

skills. Employers who lack information on the potential productivity of joh applicants

may use various proxies for these skills. Fducation, for example, may he an indicator of

prod|uctivity if individuals learn skills in school which may he applied to the job. This is

the assumption of the human capital model. 1 Alternatively, if individuals with more

"*ahility" are more likely to have additional education through a sorting mechanism,

education and productivity will he positively correlated. Employers may then use

education to screen applicants. 2 Education, experience, scholastic achievement and other

productivity proxies may he useful for predicting performance on the job.

One alternative to using general perforradnce or skill proxies is to develop

instruments which could he expected to predict performance. For examp•le, college

entrance exams are use-i to predict scholastic success. Another example is the test

which the Armed rorces administer each year to hundreds of thousands of youths. The

Armed Forces Qualifications Test (AFQT) is designed to measure the trainahility of

applicants. The test is used by all the services to screen out individuals who might he

expected to fail training in their MOS. (Army johs are classified into Military

Occupational Specialties (MOS)).

U.S. Army Research Institute, 50f1 Eisenhower Avenue, Alexandria, VA 22333. The

author is grateful to MAI Thomas Daula for helpful suggestions, and to colleagues for

comments on an earlier version. The views expressed are solely those of the author.

001
This screening device can be de'ended only if a relationship exists hetween the test score

and job or MOS performance, since the objective of the selection process is to Acquire

recruits who will perform well as soldiers rather than as students. This analysis

demonstrates that AFOT score is positively relat~d to MOI-specific performance. This

conclusion has specfic policy Implications. Current Army recruiting policy requires

substantial resources to Attract high-scoring recruits, turning away many Individuals who

desire to enlist but who do poorly on the AFOT. The emphasis on the so-called high

quality recruits can only be justified if AFQT scores can he shown to he a determinant of

productivity.

Section 1 discusses the methods and uses of the AFOT and feformance tests in the

Army. Section 2 contains a discussion of the model and the data used in the analysis.

The results are discussed in section 3. Policy Implications and conclusions are presented

in section 4.

Ability and Performance

The purpose of this analysis is to relate the Army's measure of trainability to job or

MOS performance measures. The trainability measure will he discussed first, followed hy

a discussion of performance measures.

Ability Measurement

One can argue that 'ahility' Is too broad and ambiguous to he measured well on a

one-dimensional scale. Nor is ability the Army's primary concern. The Army takes

recruits, many with no prior work experience, and trains them in a particular IAOS.

Advanced individual training is accomplished subsequent to basic training and may last

from as little as six weeks to as long as tix months or more. The concept of trainability

Is well defined. The AFOT Is designed to be a general measure of trainability and is

composed of a number of sub-tests of the Armed Services Vocational Aptitude Rattery.

2

- - .- 4' *~4 *A , - ¶~ ~
Other comhinations of suhtests, known collectively as aptitude area composites, are

created to measure more narrow types of aptitude for mechanical, electronics, clerical

and other areas.

Individuals tested are assigned scores in percentile terms relative to the 1980 youth

population of 18-23 year olds. Ry law the applicants must score above the 9th percentile

to be eligible for enlistment. The number of applicants who can be recruited in the l0th

through 30th percentiles, particularly without a high school diploma, is also limited by

Congressional mandate. However, the Army attempts to recruit above-average (AFOT

of SO or above) individuals whenever possible. The percentage of non-prior service

regular Army recruits scoring in the top 50 percentiles has risen from 26.0l percent in

fiscal year 1980 to 63.4 percent in fiscal year 1984. Over the same period the number of

male recruits with high school diplomas has risen from 4A.9 percent to 89.4 percent. This

trend can he attributed partially to changes in recruiting practices as well as to changes

in the recruiting market.

Recruiting high quality individuals, defined both in terms of AFOT scores and high

school diploma status, is particularly expensive. Substantial recruiting resources are

devoted to attracting these individuals to the Army, because the Army faces

considerable competition from educational institutions, the civilian labor market, and

other services. At the same time, the Army turns away many lower quality applicants

who could be obtained at a much lower recruiting cost.

There are two easily Identified benefits to recruiting high iuality individuals.

These recruits tend to complete their tours more often than lower category recruits. It

Is costly for the Army to recruit and train soldiers who leave the Army before

completing the tour. 3 Attrition rates have reached 3nl percent in recent years. The

second benefit Is performance. As equipment used in the Army becomes increasingly,

sophisticated, Increased productivity In Army manpower will translate Into cost savings

and increased force readiness. Unfortunately, neither productivity nor performance can

____ 3
be easily measured.

Performance Measures

Manpower productivity in the Army is not easily defined. Soldiers trained for

combat might be considered most successful if they are a deterrent to war. Skills which

may be valuable during peace may be less valuable during combat, while combat skills

may produced little *output' during peacetime. However, other things constant, the

Army should prefer soldiers who can operate or maintain equipment to those who cannot,

or soldiers who can successfully distinguish between friendly and hostile aircraft and who

can hit targets to those who cannot. Although the value of 'output' is difficult to

ascertain, the Army does evaluate soldier proficiency. These proficiency tests may he

considered one type of performance measure.

This study uses two performance measures. The first source is composed of test

scores on a variety of written and hands-on tests from the Army's training schools. The

tests will be discussed in more detail in the next section. The AFOT was originally

validated on training data and should he positively associated with the training test

scores.

The second performance measure used in this study is the Skill Qualifications Test

(SQT). The SQT is currently a written (multiple choice) test created by subject matter

experts for each .OS (except for a numher of exempt b4C)S). A SQT is given each year

at four different skill levels which correspond to experience. The skill level - test is

administered to soldiers through the E-4 grade. The skill levels 2 through 4 are given to

grades E-S, E-6, and E-7 respectively. Soldiers are tested on AOS-snecific tasks which

are contained in the Soldier's Manual for each 140S.

The SQTs are not direct performance measures. Rather, they measure skills and

knowledge required for performing the tasks. It is reasonable to assume that soldiers

who score higher on the SQT, other things equal, will demonstrate better performance in

the field.

4
However, the SOTs suffer from a number of deficiencies. A U.S. General

Accounting Office report noted that the SOT is used by the Army for two somewhat

inconsistent functions4 . The SOTs were originally developed to evaluate training

programs and to indicate deficiencies in training. The tests are also used for personnel

evaluations. The test score is recorded on soldier's personnel files and is used for

promotion decisions. A score of Sn is required for promotion unless a waiver is

obtained. Because commanders are responsible for preparing their troops to do well on

the SOT for their personnel evaluations, training becomes directed toward improving

evaluations rather than improving skills, and both functions of the SOTs become less

effective.

Before the SQTs are administered each year, soldiers are given a list of the critical

tasks on which they will be tested. Those soldiers who receive refresher training may

train specifically for those tasks, and training often occurs just prior to the SQT

testing. Therefore the annual refresher training designed to maintain skills is largely

directed towards passing the current SOT. In addition, analysis of Army personnel files

indicates many missing values for SOT scores. Ahout 30 percent of the enlisted

personnel files fcr E-S had no SOT score by the end of 1984.

in spite of problems with the SQT, the test does provide a measure of skill

knowledge for a wide range of MOS. The direct link between SOTs and actual job or 040S

proficiency is not observed. However, given the nature of the tests, it seems reasonable

to assume that soldiers who demonstrate greater skill knowledge on the SOTs will

generally he more productive in the HOS. Numerous other personal attributes, some

observable and some unobservable, will also influence productivity. We attempt to

control for some of the observable characteristics in the regression equations.
The Model

There are a number of variables which are Associated with productivity in the

human capital and signalling models. Experience for example, should Increase job skills

because training takes place over time. Ed,,cation may provide ,narketahle skills and

may also act as a sorting mechanism. In this sample of enlisted soldiers there is little

variance in years of education, but variation in high school diploma status does exikt.

Graduates are probably more motivated and goal oriented.

The type of training received by soldiers is also an important determinant of

productivity. Information is not available on the quality of training, hut it is possible to

distinguish between soldiers who have been assigned to MOS in which they received their

training and those who have been assigned to MOS for which they were not trained.

Several demographic variahles included in this analysis are likely to influence SOT

scores. Opportunities for education, training or employment in the civilian sector may

differ by race or sex. The propensity or taste for military service may also differ

between these groups. The other variables included in the model cannot fully correct for

these unobserved differences between groups. Therefore both sex and race may he

significant variables in the equations.

The Reneral model is specified in the following form:

1. Performanceof(Trainability, Education, Experience, Training, Sex, Race).

The variables used as measures of these are:

Performance: Training Data, SOT scores

Trainability: AFQT scores

Education: High School diploma status

Experience: Months in service, rank

Training: Training In same M4OS

Sex: Sex

Race: White or non-white

6
The major relationships of interest in this research is between trainability and

performance. AFQT is an accepted measure of trainability. More trainable persons are

more likely to acquire the skills and know~edge required to perform their military

assignments. AFQT may also reflect abilities other than those required tor training

success which contribute directly to performance. This analysis does not distinguish

between the two processes.

Other variables, such as education and experience, may also contribute to Job

performance. These variables may have a direct effect on performance because they are

correlated with skills or knowledge or ability. Such variables may also have an indirect

through the AFQT variable: more education is associated with hilther AFOT scores, for

example. The purpose of using multivariate regression estimation is to measure the

direct contribution of each variable. Excluding variables such as education or experience

which may be significant determinants of performance leads to omitted variable hias -

the estimates of the impact of AFQT will he biased. This estimation problem has Policy

repercussions. If AFQT is positively correlated with experience and education, for

example, a unlvarlate regression of SOT score against AFOT score will exaggerate the

effect of thq AFQT variable. The considerable cost of administering the AFOT and of

recruiting high-scoring Individuals can only he defended if AFOT has an effect on

performance independent of other variables. If AFOT is a proxy for education, for

example, then education and not AFOT score should he the selection criterion.

Therefore, neither simple correlations or univariate regression results can he used to

defend manpower quality igoals specified in terms of AFOT scores. AFQT score is only

appropriate as a selection criteria If the it contibutes to soldier performance independent

of other variables.

Estimation of the relationship between performance and the explanatory variables

is complicated by the fact that AFQT score Is an imperfect measure of ability or

trainability. Therefore the observed data matrix, X, consists of the true data X plus a

7
measurement error term V;

2, X - X * V.

The true model may then he expressed as

3. y - X P u-XA * (U-),

where y and u denote the independent variable and error matrices respectively. The

least-squares estimator of the true coefficient is

4. A = (X X) Xy.

Johnson has demonstrated that the ordinary least squares estimator of 0 given in

equation 4 is inconsistent and asymptotically biased because the observed data matrix is

correlated with the error term5. One method to correct for the errors-in-variahles is the

instrumental variables (IV) technique. An instrumental varlahles estimator will he

consistent and asymptotically unhaised. If the matrix Z is an instrument which is

uncorrelated with the errors such that plim (1 /n Z u) - 0, the IV estimator of

Ot is b, where

S. b- (Z X) Zy

and the asymptotic variance of b is

6. asy var (b) a r2 C-X) I 7 (X 0)

8
where T denotes the variance of the error term (u).

The instrument chosen for AFOT score in this study is the rank ordering of scores,

where the lowest score receives a value of one and the highest score receives a valtie of

&b This is shown as a rlurbin instrument 6 . This instrument Is correlated with AFOT

score, but is uncorrelated with the errors. The other explanatory variables are used as

their own instruments.

Data

This analysis uses two data sets. The first data set is training data for selected

MOS. The second data set includes SQT scores from the 1983 test.

Training Data

The training data (from the Army Training Centers) included a number of MOS

from several missile systems. The PERSHINC II missile data covered absic maintanence

testing for MOS 1SE and 21C (MOS descriptions are provided in the appendix) and was

created from multiple-choice questions. The STINGER antiaircraft system (MOS 16S)

testing included a written test on system knowledge, preventive maintanence, system

characteristics and other operations; a range ring profile test of aircraft type, range ring

coverage, and correct action; two visual aircraft recognition tests (photo test and test

from slides) and two hands-on tests. The first hands-on test was probability of

completing launch sequence in a moving target simulator, the second was time-to-fire.

The LANCE testing consisted of several tests for MOS 1517) and 151. The written test

questions were taken from LANCE manuals and SQTs. A map reading test consisted of

17 multiple choice questions. Each LANCE AMOS was also given hands-on operations and

maintanence tests. The HAWK missile system MIOS were given written and hands-on

tests for general equipment knowledge (160 & 16E) and equipment maintanence (24C,

9
24E, 24C).

These hand-on tests are MOS-specific. Soldiers responsible for maintenance, for

example, are asked to perform system checks, or to diagnose and repair faults which

have been Inserted In the equipment. Operators are asked to Identify ircraft from

pictures or slides, make the cortect firing decision, and complete the launch sequence.

The tests are carried out at the training centers and are designed to reflect the tasks

which the snldiers will perform. The tests are created with the assistance of system

experts.

The training data set contains a nurnber of variables which may he used as

explanatory variables in the regession. These include sex, race, education, and training

information for each soldier. Limited information on length of service, which is an

experience proxy, is available for some of the 44OS. Much of the time-in-service data is

missing, hut rank data are available. Rank is largely a function of time in service,

though more productive soldiers should he expected to he promoted more rapidly7 .

Therefore rank should, and in fact does appear to, have more explanatory power than

time in service alone. Rank cannot be strictly interpreted as an experience variable, but

is used as a proxy for experience. This variable is used in the training regressions.

The education variable has little variation. Few members of the sample have

attended college. Past research has shown that the education variable which appears to

have the most impact on soldier behavior is high school diploma status. This is not only

an Indicator for the amount of education, but may also reflect an individual's tenacity

and letermination to reach goals. The training variable (SAM E14OS) indicates whether

an Individual received training in the sarme MOS covered by the SOT test. Cenerallv, nne

would expect that training in the same MOS, high school diploma and rank should all

exert a positive influence on 30T score.

The trainability variable used in this study is AFOT score. The applicants are

required to take the AFQT to enter the Army, so scores should be available for each

10
soldier. Soldiers who took the AFOT originally between 1976 and 19R0 received scores

that were misnormed, but they have been renormed for this analysis. MOS performance

can he considered the joint output of trainahillity of the individual and the training

program. Civen a fixed training program, the AFQT score is expected to have a positive

Impact on MOS performance.

Some assumptions behind the analysis should he made explict. In particular, the

training program is held constant. It is likely that changes in the training program will

affect the impact of AFQT on performance. The more effective the training program,

the more proficient the soldiers should become. Trainability could become less

important as training effectiveness improves, though some tradeoff hetween trainahility

and performance would he anticipated with any training program. The estimated impact

of AFQT on these performance measures is conditional on the training received by the

soldiers. Consistent AFQT effects across MCOS would indicate that the trainahility is

important under a range of training programs.

SQT Data

The SQT analysis utilized the skill level 2 SQTs (rank E-5). Many of those who had

taken skill level 1 SQTs had taken different versions of the AFQT which are not strictly

comparable, though preliminary analysis showed that these SQT results were similar.

The SQT level 3 and 4 samples were relatively small. A sample of almost 53,Olfl

observations was available for skill level 2 tests.

SQT scores are recorded in the Army's enlisted personnel files, called the Enlisted

Master File (EMF). Other available variahles that could he expected to inflhuence

performance include AFQT scores, education, training, race, and time-in-service. The

variables used in the regression analysis are similar to those used In the training data

analysis. The results for several earge, representative A40S are presented in this paper;

these are generally consistent with the results for other MOS not reported here. The

11
MOS descriptions are provided in the appendix.

Results

Training Results

The Training equations are provided in Tables 1 through 3 for several weapons

systems: the HAWK, STINGER, PERSHING II, and LANCE. The equations for the two

types of tests, written and hands-on, are provided separately.

The results of the written tests (shown in Table 1) demonstrate that the AFQT

variable is consistently significant across systems and MOS. The lack of a high school

diploma is significantly negative in only one equation. Rank is always positive and

generally significant. The race coefficient varies, being significantly positive in one

equation and negative in two. The training in the MOS has mixed effect.

The two variables which stand out consistently are AFOT score and rank. In the

written tests, AFQT score is positive and significant in 10 of 11 equations, while rank is

positive and significant in 9 of the equations. No other variables are as consistent across

equations, either in sign or significance. Trainability and experience appear to he the

major determinants of the written test scores in these equations.

The hands-on tests which are reported in Tables 2 and 3 are less conclusive. AFOT

is significantly positive in 6 of the 11 equations. The pattern is similar for rank. The

negative coefficient for the time-to-fire test is consistent with the other results, since

brighter and more experienced soldiers should he able to fire the mechanism in less

time. The effect of the other variables tends to vary across H OS.

12
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0 * *~ ,. 0 ~ V;

C~ SC %0' i-.. 'u15.
SOT Results

The SQT results are demonstrated for several MOS which are generally

representative of MOS within the Army, and which are of sufficient size to provide more

precise parameter estimates. The results, shown in Table 4, are in many ways similar to

the training results. The AFQT variable is significant and positive in all the MOS, with

the coefficient ranging from .07 to .26. The coefficient of .26 implies that for each 4

additional points on the AFQT scored hy a soldier, one would expect the SOT score to

increase by about 1 point on average.

The high school diploma variable is less important than the AFQT variable as a

predictor of SQT scores. The lack of a diploma is insignificant in all of the six

equations. The signs of the other variables are generally consistent across equations.

The experience variable, time-in-service, has a positive effect, as does training in the

same MOS (SAMEMOS). The effect of sex generally varies across P.iOS. The race

variable is significant in four of the six equations; the non-white group '-as a negative

impact.

Conclusions

The results of the analysis on the TRASANA training data and the 1983 skill level 2

SQT data demonstrate that AFOT, a measure of trainability, is a significant predictor of

performance in the Army. The performance and skill measures used in this study are

imperfect, hut the consistency of the relationship across types of performance measures

and across MOS is 'n-pressive. The analysis of the SOT skill level 2 data is reported for

several large representative MOS, but the pattern is also consistent for almost all ý40S

having sufficient observations to permit analysis. Trainahility, as measured by AFOT,

16
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V. V

lz a- S

qp.r II-cd & r: o w- . 0 C

e- - C; N.- 

S* K 10 W. %CS

-~~~f %N %f qwA 

~-c C I- ec C N

*. ~-cc

rs -. - 17
exerts a positive and significant influence on Army SOT performance, holding the effect

of other variables constant. No other variahles appear to he consistently significant

across all MOS in both data sets. These findings are consistent with another study which

also documented the positive impact of AFQT score on Army soldier performance for

tankers 8. These results support the use of AFQT as a screening device for Army

applicants.

The analysis implicitly assumes that training policies remain stable and that

increased manpower performance can be attained by attracting higher quality recruits.

The returns due to changes In quantity or quality of training cannot be estimated in this

framework. If training pdicles were significantly altered, the relationship between

AFQT and performance mi;ht be expected to change, although the direction of the

change cannot be predicted a priori The performance measures themselves are also

likely to change over time. Yet evidence of a positive relationship between performance

and skill measures on the one hand, and the AFQT on the other, Is fairly convincing. The

impact of other variables on performance Is less consistent. The high school diploma

generally does exert a positive Impact on performance, as does experience anA MC$Sspecific training.

18
1aFRKfCIKS

Baldwin, Robert H. and Thomas V. Daula. "The Cost of Righ-Ouality

Recruits." Armer Forces and Society 2, No. 1 (Fall 1984): 96-114.

Becker, Gary S. Human Capital. Second Edition. New York: Columbia

University Press for Nat. Bur. Econ. Res.), 1975.

Johnson, J. Econometric Methods, 2nd Edition. New York: McGraw Hill,

1972.

Mincer, Jacob A. Schooling, Experience ind Earnings. New York:

Columbia University Press (for Nat. Bur. Econ. Res.), 1974.

Nord, Roy and Thomas ')aula, Estimated Time to Promotion for Enlisted

Soldiers, paper presented at The Information Management/Operations

Research Society of America Conference, Boston, MA; July 1985.

Riley, John G. "Testing the Educational Screening Hypothesis." J.P.E.

87, No. 5, 2(October 1979): S227-S252.

Scribner, Barry L., D. Alton Smith, Robert H. Baldwin and Robert W.

Phillips. Are Smart Tankers Better Tankers: AFQT and Military

Productivity. Office of Economic and Maniower Analysis, Department

of Social Sciences, U.S. Military Academy, December 1984.

Stiglitz, Joseph E. "The Theory of Screening, Education, and the

Distribution of Income." American Economic Review 65 (June 1975):

283-300.

U.S. General Accounting Office, Report to the Secretary of the Army.

The Army Needs to Modify its System for Measuring Individual

Soldier Status. Report to the Secretary of the Army. March 30, 1982.

Weiss, Andrew. "A Sorting-cum-Learning Model of Education." Journal of

Political Economy 91,No. 3, (June 1983): 420-442.

19
Appendix

MOS Descriptions

MOS Description

05C Radio Operator

i18 Infantryman

15D Lance Crewmember

15E Pershing Missile Crevmember

16D Hawk Missile Crewman

16E Hawk Fire Control Crewmember

16S Stinger Crewman

21G Pershing Electric Maintenance Specialist

24C Hawk Firing Section Mechanic

24G Hawk Coordination Central Mechanic

63B Light Vehicle/Power Generation Mechanic

75B Personnel Administration Specialist

94B Food Service Specialist

980 Electronic Warfare/Signal Intelligence V

20
NOTES

1. Expositions of the human capital model Include Gary S. Recker, Human Capital.

Second Edition. New York: Columbia University Press (for Nat. Rut. Econ. Res.),

1975, and Jacob A. Mincer, Schoolin Experience and-Earnings. New York: Columbia

University Press (for Nat. But. Econ. Res.), 1974.

2. See, for example, loseph E. Stiglitz. 'The Theory of ScreeningEducation, and the

Distrihution of Income.* American Economic Review 65 (june 1975): 28i-3o0f, John

C. Riley, 'Testing the Educational Screening Hypothesis." journal of PoliticAl

Econom. 87, No. 5, 2(October 1979): S227-S252, and Andrew Weiss, 'A Sorting-cumLearning Model of Education.* Jonrnal of Political Fconomy 91, No. 3, (June lqR3):

420-442.

3. This argument Is made hy Robert H. Baldwin and Thomas V. I)aula, 'The Cost o•f

High-Qualltv Recruits,* Armed Forces and Society II, No. 1 (Fall 1984): 96-114.

4. See U.S. General Accounting Office, Report to the Secretary of the Army. The

Army Needs to Modify its System for Measuring Individual Soldier Status. Report to

the Secretary of the Army. March 30, 1982.

S. J. Johnson. Econometric Methods, 2nd Edition. New York: McGraw-Hill, 1q72, orp.

278-291.

6. Ibld, pp. 285-286.

21
7. This was found in Roy Nord and Thomas V. laula, Fstimated Time to Pronmotion for

Enlisted Soldiers paper presented at The Information Management/Onerations

Research Society of America Conference, Roston, 0.4A; July 1985.

8. Barry L. Scrihner, D). Alton Smith, Robert H. Raidwin and Rohert W. Phillips, Are

Smart Tankers Better Tankers: AFOT and M4ilitary Productivity, Office of Economic

and Manpower Analysis, Department of Social Sciences, United States tilitary

Academy, December 1984. The paper is reproduced in the Report to the House and

Senate Committees on Armed Services, Defense 04anpowerOuality Volume II (Army

Submission), May 1985 appendix C. Preliminary regression equations for the training

and SOT data, generated hy David K. Home and Major Thomas V. flaula, are

included in appendix E and appendix I of the same report.

22